diff options
Diffstat (limited to 'src/web/api/queries/weights.c')
| -rw-r--r-- | src/web/api/queries/weights.c | 2105 |
1 files changed, 2105 insertions, 0 deletions
diff --git a/src/web/api/queries/weights.c b/src/web/api/queries/weights.c new file mode 100644 index 00000000..44928fea --- /dev/null +++ b/src/web/api/queries/weights.c @@ -0,0 +1,2105 @@ +// SPDX-License-Identifier: GPL-3.0-or-later + +#include "daemon/common.h" +#include "database/KolmogorovSmirnovDist.h" + +#define MAX_POINTS 10000 +int enable_metric_correlations = CONFIG_BOOLEAN_YES; +int metric_correlations_version = 1; +WEIGHTS_METHOD default_metric_correlations_method = WEIGHTS_METHOD_MC_KS2; + +typedef struct weights_stats { + NETDATA_DOUBLE max_base_high_ratio; + size_t db_points; + size_t result_points; + size_t db_queries; + size_t db_points_per_tier[RRD_STORAGE_TIERS]; + size_t binary_searches; +} WEIGHTS_STATS; + +// ---------------------------------------------------------------------------- +// parse and render metric correlations methods + +static struct { + const char *name; + WEIGHTS_METHOD value; +} weights_methods[] = { + { "ks2" , WEIGHTS_METHOD_MC_KS2} + , { "volume" , WEIGHTS_METHOD_MC_VOLUME} + , { "anomaly-rate" , WEIGHTS_METHOD_ANOMALY_RATE} + , { "value" , WEIGHTS_METHOD_VALUE} + , { NULL , 0 } +}; + +WEIGHTS_METHOD weights_string_to_method(const char *method) { + for(int i = 0; weights_methods[i].name ;i++) + if(strcmp(method, weights_methods[i].name) == 0) + return weights_methods[i].value; + + return default_metric_correlations_method; +} + +const char *weights_method_to_string(WEIGHTS_METHOD method) { + for(int i = 0; weights_methods[i].name ;i++) + if(weights_methods[i].value == method) + return weights_methods[i].name; + + return "unknown"; +} + +// ---------------------------------------------------------------------------- +// The results per dimension are aggregated into a dictionary + +typedef enum { + RESULT_IS_BASE_HIGH_RATIO = (1 << 0), + RESULT_IS_PERCENTAGE_OF_TIME = (1 << 1), +} RESULT_FLAGS; + +struct register_result { + RESULT_FLAGS flags; + RRDHOST *host; + RRDCONTEXT_ACQUIRED *rca; + RRDINSTANCE_ACQUIRED *ria; + RRDMETRIC_ACQUIRED *rma; + NETDATA_DOUBLE value; + STORAGE_POINT highlighted; + STORAGE_POINT baseline; + usec_t duration_ut; +}; + +static DICTIONARY *register_result_init() { + DICTIONARY *results = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_FIXED_SIZE, NULL, sizeof(struct register_result)); + return results; +} + +static void register_result_destroy(DICTIONARY *results) { + dictionary_destroy(results); +} + +static void register_result(DICTIONARY *results, RRDHOST *host, RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, + RRDMETRIC_ACQUIRED *rma, NETDATA_DOUBLE value, RESULT_FLAGS flags, + STORAGE_POINT *highlighted, STORAGE_POINT *baseline, WEIGHTS_STATS *stats, + bool register_zero, usec_t duration_ut) { + + if(!netdata_double_isnumber(value)) return; + + // make it positive + NETDATA_DOUBLE v = fabsndd(value); + + // no need to store zero scored values + if(unlikely(fpclassify(v) == FP_ZERO && !register_zero)) + return; + + // keep track of the max of the baseline / highlight ratio + if((flags & RESULT_IS_BASE_HIGH_RATIO) && v > stats->max_base_high_ratio) + stats->max_base_high_ratio = v; + + struct register_result t = { + .flags = flags, + .host = host, + .rca = rca, + .ria = ria, + .rma = rma, + .value = v, + .duration_ut = duration_ut, + }; + + if(highlighted) + t.highlighted = *highlighted; + + if(baseline) + t.baseline = *baseline; + + // we can use the pointer address or RMA as a unique key for each metric + char buf[20 + 1]; + ssize_t len = snprintfz(buf, sizeof(buf) - 1, "%p", rma); + dictionary_set_advanced(results, buf, len, &t, sizeof(struct register_result), NULL); +} + +// ---------------------------------------------------------------------------- +// Generation of JSON output for the results + +static void results_header_to_json(DICTIONARY *results __maybe_unused, BUFFER *wb, + time_t after, time_t before, + time_t baseline_after, time_t baseline_before, + size_t points, WEIGHTS_METHOD method, + RRDR_TIME_GROUPING group, RRDR_OPTIONS options, uint32_t shifts, + size_t examined_dimensions __maybe_unused, usec_t duration, + WEIGHTS_STATS *stats) { + + buffer_json_member_add_time_t(wb, "after", after); + buffer_json_member_add_time_t(wb, "before", before); + buffer_json_member_add_time_t(wb, "duration", before - after); + buffer_json_member_add_uint64(wb, "points", points); + + if(method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME) { + buffer_json_member_add_time_t(wb, "baseline_after", baseline_after); + buffer_json_member_add_time_t(wb, "baseline_before", baseline_before); + buffer_json_member_add_time_t(wb, "baseline_duration", baseline_before - baseline_after); + buffer_json_member_add_uint64(wb, "baseline_points", points << shifts); + } + + buffer_json_member_add_object(wb, "statistics"); + { + buffer_json_member_add_double(wb, "query_time_ms", (double) duration / (double) USEC_PER_MS); + buffer_json_member_add_uint64(wb, "db_queries", stats->db_queries); + buffer_json_member_add_uint64(wb, "query_result_points", stats->result_points); + buffer_json_member_add_uint64(wb, "binary_searches", stats->binary_searches); + buffer_json_member_add_uint64(wb, "db_points_read", stats->db_points); + + buffer_json_member_add_array(wb, "db_points_per_tier"); + { + for (size_t tier = 0; tier < storage_tiers; tier++) + buffer_json_add_array_item_uint64(wb, stats->db_points_per_tier[tier]); + } + buffer_json_array_close(wb); + } + buffer_json_object_close(wb); + + buffer_json_member_add_string(wb, "group", time_grouping_tostring(group)); + buffer_json_member_add_string(wb, "method", weights_method_to_string(method)); + rrdr_options_to_buffer_json_array(wb, "options", options); +} + +static size_t registered_results_to_json_charts(DICTIONARY *results, BUFFER *wb, + time_t after, time_t before, + time_t baseline_after, time_t baseline_before, + size_t points, WEIGHTS_METHOD method, + RRDR_TIME_GROUPING group, RRDR_OPTIONS options, uint32_t shifts, + size_t examined_dimensions, usec_t duration, + WEIGHTS_STATS *stats) { + + buffer_json_initialize(wb, "\"", "\"", 0, true, (options & RRDR_OPTION_MINIFY) ? BUFFER_JSON_OPTIONS_MINIFY : BUFFER_JSON_OPTIONS_DEFAULT); + + results_header_to_json(results, wb, after, before, baseline_after, baseline_before, + points, method, group, options, shifts, examined_dimensions, duration, stats); + + buffer_json_member_add_object(wb, "correlated_charts"); + + size_t charts = 0, total_dimensions = 0; + struct register_result *t; + RRDINSTANCE_ACQUIRED *last_ria = NULL; // never access this - we use it only for comparison + dfe_start_read(results, t) { + if(t->ria != last_ria) { + last_ria = t->ria; + + if(charts) { + buffer_json_object_close(wb); // dimensions + buffer_json_object_close(wb); // chart:id + } + + buffer_json_member_add_object(wb, rrdinstance_acquired_id(t->ria)); + buffer_json_member_add_string(wb, "context", rrdcontext_acquired_id(t->rca)); + buffer_json_member_add_object(wb, "dimensions"); + charts++; + } + buffer_json_member_add_double(wb, rrdmetric_acquired_name(t->rma), t->value); + total_dimensions++; + } + dfe_done(t); + + // close dimensions and chart + if (total_dimensions) { + buffer_json_object_close(wb); // dimensions + buffer_json_object_close(wb); // chart:id + } + + buffer_json_object_close(wb); + + buffer_json_member_add_uint64(wb, "correlated_dimensions", total_dimensions); + buffer_json_member_add_uint64(wb, "total_dimensions_count", examined_dimensions); + buffer_json_finalize(wb); + + return total_dimensions; +} + +static size_t registered_results_to_json_contexts(DICTIONARY *results, BUFFER *wb, + time_t after, time_t before, + time_t baseline_after, time_t baseline_before, + size_t points, WEIGHTS_METHOD method, + RRDR_TIME_GROUPING group, RRDR_OPTIONS options, uint32_t shifts, + size_t examined_dimensions, usec_t duration, + WEIGHTS_STATS *stats) { + + buffer_json_initialize(wb, "\"", "\"", 0, true, (options & RRDR_OPTION_MINIFY) ? BUFFER_JSON_OPTIONS_MINIFY : BUFFER_JSON_OPTIONS_DEFAULT); + + results_header_to_json(results, wb, after, before, baseline_after, baseline_before, + points, method, group, options, shifts, examined_dimensions, duration, stats); + + buffer_json_member_add_object(wb, "contexts"); + + size_t contexts = 0, charts = 0, total_dimensions = 0, context_dims = 0, chart_dims = 0; + NETDATA_DOUBLE contexts_total_weight = 0.0, charts_total_weight = 0.0; + struct register_result *t; + RRDCONTEXT_ACQUIRED *last_rca = NULL; + RRDINSTANCE_ACQUIRED *last_ria = NULL; + dfe_start_read(results, t) { + + if(t->rca != last_rca) { + last_rca = t->rca; + + if(contexts) { + buffer_json_object_close(wb); // dimensions + buffer_json_member_add_double(wb, "weight", charts_total_weight / (double) chart_dims); + buffer_json_object_close(wb); // chart:id + buffer_json_object_close(wb); // charts + buffer_json_member_add_double(wb, "weight", contexts_total_weight / (double) context_dims); + buffer_json_object_close(wb); // context + } + + buffer_json_member_add_object(wb, rrdcontext_acquired_id(t->rca)); + buffer_json_member_add_object(wb, "charts"); + + contexts++; + charts = 0; + context_dims = 0; + contexts_total_weight = 0.0; + + last_ria = NULL; + } + + if(t->ria != last_ria) { + last_ria = t->ria; + + if(charts) { + buffer_json_object_close(wb); // dimensions + buffer_json_member_add_double(wb, "weight", charts_total_weight / (double) chart_dims); + buffer_json_object_close(wb); // chart:id + } + + buffer_json_member_add_object(wb, rrdinstance_acquired_id(t->ria)); + buffer_json_member_add_object(wb, "dimensions"); + + charts++; + chart_dims = 0; + charts_total_weight = 0.0; + } + + buffer_json_member_add_double(wb, rrdmetric_acquired_name(t->rma), t->value); + charts_total_weight += t->value; + contexts_total_weight += t->value; + chart_dims++; + context_dims++; + total_dimensions++; + } + dfe_done(t); + + // close dimensions and chart + if (total_dimensions) { + buffer_json_object_close(wb); // dimensions + buffer_json_member_add_double(wb, "weight", charts_total_weight / (double) chart_dims); + buffer_json_object_close(wb); // chart:id + buffer_json_object_close(wb); // charts + buffer_json_member_add_double(wb, "weight", contexts_total_weight / (double) context_dims); + buffer_json_object_close(wb); // context + } + + buffer_json_object_close(wb); + + buffer_json_member_add_uint64(wb, "correlated_dimensions", total_dimensions); + buffer_json_member_add_uint64(wb, "total_dimensions_count", examined_dimensions); + buffer_json_finalize(wb); + + return total_dimensions; +} + +struct query_weights_data { + QUERY_WEIGHTS_REQUEST *qwr; + + SIMPLE_PATTERN *scope_nodes_sp; + SIMPLE_PATTERN *scope_contexts_sp; + SIMPLE_PATTERN *nodes_sp; + SIMPLE_PATTERN *contexts_sp; + SIMPLE_PATTERN *instances_sp; + SIMPLE_PATTERN *dimensions_sp; + SIMPLE_PATTERN *labels_sp; + SIMPLE_PATTERN *alerts_sp; + + usec_t timeout_us; + bool timed_out; + bool interrupted; + + struct query_timings timings; + + size_t examined_dimensions; + bool register_zero; + + DICTIONARY *results; + WEIGHTS_STATS stats; + + uint32_t shifts; + + struct query_versions versions; +}; + +#define AGGREGATED_WEIGHT_EMPTY (struct aggregated_weight) { \ + .min = NAN, \ + .max = NAN, \ + .sum = NAN, \ + .count = 0, \ + .hsp = STORAGE_POINT_UNSET, \ + .bsp = STORAGE_POINT_UNSET, \ +} + +#define merge_into_aw(aw, t) do { \ + if(!(aw).count) { \ + (aw).count = 1; \ + (aw).min = (aw).max = (aw).sum = (t)->value; \ + (aw).hsp = (t)->highlighted; \ + if(baseline) \ + (aw).bsp = (t)->baseline; \ + } \ + else { \ + (aw).count++; \ + (aw).sum += (t)->value; \ + if((t)->value < (aw).min) \ + (aw).min = (t)->value; \ + if((t)->value > (aw).max) \ + (aw).max = (t)->value; \ + storage_point_merge_to((aw).hsp, (t)->highlighted); \ + if(baseline) \ + storage_point_merge_to((aw).bsp, (t)->baseline); \ + } \ +} while(0) + +static void results_header_to_json_v2(DICTIONARY *results __maybe_unused, BUFFER *wb, struct query_weights_data *qwd, + time_t after, time_t before, + time_t baseline_after, time_t baseline_before, + size_t points, WEIGHTS_METHOD method, + RRDR_TIME_GROUPING group, RRDR_OPTIONS options, uint32_t shifts, + size_t examined_dimensions __maybe_unused, usec_t duration __maybe_unused, + WEIGHTS_STATS *stats, bool group_by) { + + buffer_json_member_add_object(wb, "request"); + buffer_json_member_add_string(wb, "method", weights_method_to_string(method)); + rrdr_options_to_buffer_json_array(wb, "options", options); + + buffer_json_member_add_object(wb, "scope"); + buffer_json_member_add_string(wb, "scope_nodes", qwd->qwr->scope_nodes ? qwd->qwr->scope_nodes : "*"); + buffer_json_member_add_string(wb, "scope_contexts", qwd->qwr->scope_contexts ? qwd->qwr->scope_contexts : "*"); + buffer_json_object_close(wb); + + buffer_json_member_add_object(wb, "selectors"); + buffer_json_member_add_string(wb, "nodes", qwd->qwr->nodes ? qwd->qwr->nodes : "*"); + buffer_json_member_add_string(wb, "contexts", qwd->qwr->contexts ? qwd->qwr->contexts : "*"); + buffer_json_member_add_string(wb, "instances", qwd->qwr->instances ? qwd->qwr->instances : "*"); + buffer_json_member_add_string(wb, "dimensions", qwd->qwr->dimensions ? qwd->qwr->dimensions : "*"); + buffer_json_member_add_string(wb, "labels", qwd->qwr->labels ? qwd->qwr->labels : "*"); + buffer_json_member_add_string(wb, "alerts", qwd->qwr->alerts ? qwd->qwr->alerts : "*"); + buffer_json_object_close(wb); + + buffer_json_member_add_object(wb, "window"); + buffer_json_member_add_time_t(wb, "after", qwd->qwr->after); + buffer_json_member_add_time_t(wb, "before", qwd->qwr->before); + buffer_json_member_add_uint64(wb, "points", qwd->qwr->points); + if(qwd->qwr->options & RRDR_OPTION_SELECTED_TIER) + buffer_json_member_add_uint64(wb, "tier", qwd->qwr->tier); + else + buffer_json_member_add_string(wb, "tier", NULL); + buffer_json_object_close(wb); + + if(method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME) { + buffer_json_member_add_object(wb, "baseline"); + buffer_json_member_add_time_t(wb, "baseline_after", qwd->qwr->baseline_after); + buffer_json_member_add_time_t(wb, "baseline_before", qwd->qwr->baseline_before); + buffer_json_object_close(wb); + } + + buffer_json_member_add_object(wb, "aggregations"); + buffer_json_member_add_object(wb, "time"); + buffer_json_member_add_string(wb, "time_group", time_grouping_tostring(qwd->qwr->time_group_method)); + buffer_json_member_add_string(wb, "time_group_options", qwd->qwr->time_group_options); + buffer_json_object_close(wb); // time + + buffer_json_member_add_array(wb, "metrics"); + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_array(wb, "group_by"); + buffer_json_group_by_to_array(wb, qwd->qwr->group_by.group_by); + buffer_json_array_close(wb); + +// buffer_json_member_add_array(wb, "group_by_label"); +// buffer_json_array_close(wb); + + buffer_json_member_add_string(wb, "aggregation", group_by_aggregate_function_to_string(qwd->qwr->group_by.aggregation)); + } + buffer_json_object_close(wb); // 1st group by + buffer_json_array_close(wb); // array + buffer_json_object_close(wb); // aggregations + + buffer_json_member_add_uint64(wb, "timeout", qwd->qwr->timeout_ms); + buffer_json_object_close(wb); // request + + buffer_json_member_add_object(wb, "view"); + buffer_json_member_add_string(wb, "format", (group_by)?"grouped":"full"); + buffer_json_member_add_string(wb, "time_group", time_grouping_tostring(group)); + + buffer_json_member_add_object(wb, "window"); + buffer_json_member_add_time_t(wb, "after", after); + buffer_json_member_add_time_t(wb, "before", before); + buffer_json_member_add_time_t(wb, "duration", before - after); + buffer_json_member_add_uint64(wb, "points", points); + buffer_json_object_close(wb); + + if(method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME) { + buffer_json_member_add_object(wb, "baseline"); + buffer_json_member_add_time_t(wb, "after", baseline_after); + buffer_json_member_add_time_t(wb, "before", baseline_before); + buffer_json_member_add_time_t(wb, "duration", baseline_before - baseline_after); + buffer_json_member_add_uint64(wb, "points", points << shifts); + buffer_json_object_close(wb); + } + + buffer_json_object_close(wb); // view + + buffer_json_member_add_object(wb, "db"); + { + buffer_json_member_add_uint64(wb, "db_queries", stats->db_queries); + buffer_json_member_add_uint64(wb, "query_result_points", stats->result_points); + buffer_json_member_add_uint64(wb, "binary_searches", stats->binary_searches); + buffer_json_member_add_uint64(wb, "db_points_read", stats->db_points); + + buffer_json_member_add_array(wb, "db_points_per_tier"); + { + for (size_t tier = 0; tier < storage_tiers; tier++) + buffer_json_add_array_item_uint64(wb, stats->db_points_per_tier[tier]); + } + buffer_json_array_close(wb); + } + buffer_json_object_close(wb); // db +} + +typedef enum { + WPT_DIMENSION = 0, + WPT_INSTANCE = 1, + WPT_CONTEXT = 2, + WPT_NODE = 3, + WPT_GROUP = 4, +} WEIGHTS_POINT_TYPE; + +struct aggregated_weight { + const char *name; + NETDATA_DOUBLE min; + NETDATA_DOUBLE max; + NETDATA_DOUBLE sum; + size_t count; + STORAGE_POINT hsp; + STORAGE_POINT bsp; +}; + +static inline void storage_point_to_json(BUFFER *wb, WEIGHTS_POINT_TYPE type, ssize_t di, ssize_t ii, ssize_t ci, ssize_t ni, struct aggregated_weight *aw, RRDR_OPTIONS options __maybe_unused, bool baseline) { + if(type != WPT_GROUP) { + buffer_json_add_array_item_array(wb); + buffer_json_add_array_item_uint64(wb, type); // "type" + buffer_json_add_array_item_int64(wb, ni); + if (type != WPT_NODE) { + buffer_json_add_array_item_int64(wb, ci); + if (type != WPT_CONTEXT) { + buffer_json_add_array_item_int64(wb, ii); + if (type != WPT_INSTANCE) + buffer_json_add_array_item_int64(wb, di); + else + buffer_json_add_array_item_string(wb, NULL); + } + else { + buffer_json_add_array_item_string(wb, NULL); + buffer_json_add_array_item_string(wb, NULL); + } + } + else { + buffer_json_add_array_item_string(wb, NULL); + buffer_json_add_array_item_string(wb, NULL); + buffer_json_add_array_item_string(wb, NULL); + } + buffer_json_add_array_item_double(wb, (aw->count) ? aw->sum / (NETDATA_DOUBLE)aw->count : 0.0); // "weight" + } + else { + buffer_json_member_add_array(wb, "v"); + buffer_json_add_array_item_array(wb); + buffer_json_add_array_item_double(wb, aw->min); // "min" + buffer_json_add_array_item_double(wb, (aw->count) ? aw->sum / (NETDATA_DOUBLE)aw->count : 0.0); // "avg" + buffer_json_add_array_item_double(wb, aw->max); // "max" + buffer_json_add_array_item_double(wb, aw->sum); // "sum" + buffer_json_add_array_item_uint64(wb, aw->count); // "count" + buffer_json_array_close(wb); + } + + buffer_json_add_array_item_array(wb); + buffer_json_add_array_item_double(wb, aw->hsp.min); // "min" + buffer_json_add_array_item_double(wb, (aw->hsp.count) ? aw->hsp.sum / (NETDATA_DOUBLE) aw->hsp.count : 0.0); // "avg" + buffer_json_add_array_item_double(wb, aw->hsp.max); // "max" + buffer_json_add_array_item_double(wb, aw->hsp.sum); // "sum" + buffer_json_add_array_item_uint64(wb, aw->hsp.count); // "count" + buffer_json_add_array_item_uint64(wb, aw->hsp.anomaly_count); // "anomaly_count" + buffer_json_array_close(wb); + + if(baseline) { + buffer_json_add_array_item_array(wb); + buffer_json_add_array_item_double(wb, aw->bsp.min); // "min" + buffer_json_add_array_item_double(wb, (aw->bsp.count) ? aw->bsp.sum / (NETDATA_DOUBLE) aw->bsp.count : 0.0); // "avg" + buffer_json_add_array_item_double(wb, aw->bsp.max); // "max" + buffer_json_add_array_item_double(wb, aw->bsp.sum); // "sum" + buffer_json_add_array_item_uint64(wb, aw->bsp.count); // "count" + buffer_json_add_array_item_uint64(wb, aw->bsp.anomaly_count); // "anomaly_count" + buffer_json_array_close(wb); + } + + buffer_json_array_close(wb); +} + +static void multinode_data_schema(BUFFER *wb, RRDR_OPTIONS options __maybe_unused, const char *key, bool baseline, bool group_by) { + buffer_json_member_add_object(wb, key); // schema + + buffer_json_member_add_string(wb, "type", "array"); + buffer_json_member_add_array(wb, "items"); + + if(group_by) { + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "weight"); + buffer_json_member_add_string(wb, "type", "array"); + buffer_json_member_add_array(wb, "labels"); + { + buffer_json_add_array_item_string(wb, "min"); + buffer_json_add_array_item_string(wb, "avg"); + buffer_json_add_array_item_string(wb, "max"); + buffer_json_add_array_item_string(wb, "sum"); + buffer_json_add_array_item_string(wb, "count"); + } + buffer_json_array_close(wb); + } + buffer_json_object_close(wb); + } + else { + buffer_json_add_array_item_object(wb); + buffer_json_member_add_string(wb, "name", "row_type"); + buffer_json_member_add_string(wb, "type", "integer"); + buffer_json_member_add_array(wb, "value"); + buffer_json_add_array_item_string(wb, "dimension"); + buffer_json_add_array_item_string(wb, "instance"); + buffer_json_add_array_item_string(wb, "context"); + buffer_json_add_array_item_string(wb, "node"); + buffer_json_array_close(wb); + buffer_json_object_close(wb); + + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "ni"); + buffer_json_member_add_string(wb, "type", "integer"); + buffer_json_member_add_string(wb, "dictionary", "nodes"); + } + buffer_json_object_close(wb); + + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "ci"); + buffer_json_member_add_string(wb, "type", "integer"); + buffer_json_member_add_string(wb, "dictionary", "contexts"); + } + buffer_json_object_close(wb); + + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "ii"); + buffer_json_member_add_string(wb, "type", "integer"); + buffer_json_member_add_string(wb, "dictionary", "instances"); + } + buffer_json_object_close(wb); + + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "di"); + buffer_json_member_add_string(wb, "type", "integer"); + buffer_json_member_add_string(wb, "dictionary", "dimensions"); + } + buffer_json_object_close(wb); + + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "weight"); + buffer_json_member_add_string(wb, "type", "number"); + } + buffer_json_object_close(wb); + } + + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "timeframe"); + buffer_json_member_add_string(wb, "type", "array"); + buffer_json_member_add_array(wb, "labels"); + { + buffer_json_add_array_item_string(wb, "min"); + buffer_json_add_array_item_string(wb, "avg"); + buffer_json_add_array_item_string(wb, "max"); + buffer_json_add_array_item_string(wb, "sum"); + buffer_json_add_array_item_string(wb, "count"); + buffer_json_add_array_item_string(wb, "anomaly_count"); + } + buffer_json_array_close(wb); + buffer_json_member_add_object(wb, "calculations"); + buffer_json_member_add_string(wb, "anomaly rate", "anomaly_count * 100 / count"); + buffer_json_object_close(wb); + } + buffer_json_object_close(wb); + + if(baseline) { + buffer_json_add_array_item_object(wb); + { + buffer_json_member_add_string(wb, "name", "baseline timeframe"); + buffer_json_member_add_string(wb, "type", "array"); + buffer_json_member_add_array(wb, "labels"); + { + buffer_json_add_array_item_string(wb, "min"); + buffer_json_add_array_item_string(wb, "avg"); + buffer_json_add_array_item_string(wb, "max"); + buffer_json_add_array_item_string(wb, "sum"); + buffer_json_add_array_item_string(wb, "count"); + buffer_json_add_array_item_string(wb, "anomaly_count"); + } + buffer_json_array_close(wb); + buffer_json_member_add_object(wb, "calculations"); + buffer_json_member_add_string(wb, "anomaly rate", "anomaly_count * 100 / count"); + buffer_json_object_close(wb); + } + buffer_json_object_close(wb); + } + + buffer_json_array_close(wb); // items + buffer_json_object_close(wb); // schema +} + +struct dict_unique_node { + bool existing; + bool exposed; + uint32_t i; + RRDHOST *host; + usec_t duration_ut; +}; + +struct dict_unique_name_units { + bool existing; + bool exposed; + uint32_t i; + const char *units; +}; + +struct dict_unique_id_name { + bool existing; + bool exposed; + uint32_t i; + const char *id; + const char *name; +}; + +static inline struct dict_unique_node *dict_unique_node_add(DICTIONARY *dict, RRDHOST *host, ssize_t *max_id) { + struct dict_unique_node *dun = dictionary_set(dict, host->machine_guid, NULL, sizeof(struct dict_unique_node)); + if(!dun->existing) { + dun->existing = true; + dun->host = host; + dun->i = *max_id; + (*max_id)++; + } + + return dun; +} + +static inline struct dict_unique_name_units *dict_unique_name_units_add(DICTIONARY *dict, const char *name, const char *units, ssize_t *max_id) { + struct dict_unique_name_units *dun = dictionary_set(dict, name, NULL, sizeof(struct dict_unique_name_units)); + if(!dun->existing) { + dun->units = units; + dun->existing = true; + dun->i = *max_id; + (*max_id)++; + } + + return dun; +} + +static inline struct dict_unique_id_name *dict_unique_id_name_add(DICTIONARY *dict, const char *id, const char *name, ssize_t *max_id) { + char key[1024 + 1]; + snprintfz(key, sizeof(key) - 1, "%s:%s", id, name); + struct dict_unique_id_name *dun = dictionary_set(dict, key, NULL, sizeof(struct dict_unique_id_name)); + if(!dun->existing) { + dun->existing = true; + dun->i = *max_id; + (*max_id)++; + dun->id = id; + dun->name = name; + } + + return dun; +} + +static size_t registered_results_to_json_multinode_no_group_by( + DICTIONARY *results, BUFFER *wb, + time_t after, time_t before, + time_t baseline_after, time_t baseline_before, + size_t points, WEIGHTS_METHOD method, + RRDR_TIME_GROUPING group, RRDR_OPTIONS options, uint32_t shifts, + size_t examined_dimensions, struct query_weights_data *qwd, + WEIGHTS_STATS *stats, + struct query_versions *versions) { + buffer_json_initialize(wb, "\"", "\"", 0, true, (options & RRDR_OPTION_MINIFY) ? BUFFER_JSON_OPTIONS_MINIFY : BUFFER_JSON_OPTIONS_DEFAULT); + buffer_json_member_add_uint64(wb, "api", 2); + + results_header_to_json_v2(results, wb, qwd, after, before, baseline_after, baseline_before, + points, method, group, options, shifts, examined_dimensions, + qwd->timings.executed_ut - qwd->timings.received_ut, stats, false); + + version_hashes_api_v2(wb, versions); + + bool baseline = method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME; + multinode_data_schema(wb, options, "schema", baseline, false); + + DICTIONARY *dict_nodes = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE | DICT_OPTION_FIXED_SIZE, NULL, sizeof(struct dict_unique_node)); + DICTIONARY *dict_contexts = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE | DICT_OPTION_FIXED_SIZE, NULL, sizeof(struct dict_unique_name_units)); + DICTIONARY *dict_instances = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE | DICT_OPTION_FIXED_SIZE, NULL, sizeof(struct dict_unique_id_name)); + DICTIONARY *dict_dimensions = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE | DICT_OPTION_FIXED_SIZE, NULL, sizeof(struct dict_unique_id_name)); + + buffer_json_member_add_array(wb, "result"); + + struct aggregated_weight node_aw = AGGREGATED_WEIGHT_EMPTY, context_aw = AGGREGATED_WEIGHT_EMPTY, instance_aw = AGGREGATED_WEIGHT_EMPTY; + struct register_result *t; + RRDHOST *last_host = NULL; + RRDCONTEXT_ACQUIRED *last_rca = NULL; + RRDINSTANCE_ACQUIRED *last_ria = NULL; + struct dict_unique_name_units *context_dun = NULL; + struct dict_unique_node *node_dun = NULL; + struct dict_unique_id_name *instance_dun = NULL; + struct dict_unique_id_name *dimension_dun = NULL; + ssize_t di = -1, ii = -1, ci = -1, ni = -1; + ssize_t di_max = 0, ii_max = 0, ci_max = 0, ni_max = 0; + size_t total_dimensions = 0; + dfe_start_read(results, t) { + + // close instance + if(t->ria != last_ria && last_ria) { + storage_point_to_json(wb, WPT_INSTANCE, di, ii, ci, ni, &instance_aw, options, baseline); + instance_dun->exposed = true; + last_ria = NULL; + instance_aw = AGGREGATED_WEIGHT_EMPTY; + } + + // close context + if(t->rca != last_rca && last_rca) { + storage_point_to_json(wb, WPT_CONTEXT, di, ii, ci, ni, &context_aw, options, baseline); + context_dun->exposed = true; + last_rca = NULL; + context_aw = AGGREGATED_WEIGHT_EMPTY; + } + + // close node + if(t->host != last_host && last_host) { + storage_point_to_json(wb, WPT_NODE, di, ii, ci, ni, &node_aw, options, baseline); + node_dun->exposed = true; + last_host = NULL; + node_aw = AGGREGATED_WEIGHT_EMPTY; + } + + // open node + if(t->host != last_host) { + last_host = t->host; + node_dun = dict_unique_node_add(dict_nodes, t->host, &ni_max); + ni = node_dun->i; + } + + // open context + if(t->rca != last_rca) { + last_rca = t->rca; + context_dun = dict_unique_name_units_add(dict_contexts, rrdcontext_acquired_id(t->rca), + rrdcontext_acquired_units(t->rca), &ci_max); + ci = context_dun->i; + } + + // open instance + if(t->ria != last_ria) { + last_ria = t->ria; + instance_dun = dict_unique_id_name_add(dict_instances, rrdinstance_acquired_id(t->ria), rrdinstance_acquired_name(t->ria), &ii_max); + ii = instance_dun->i; + } + + dimension_dun = dict_unique_id_name_add(dict_dimensions, rrdmetric_acquired_id(t->rma), rrdmetric_acquired_name(t->rma), &di_max); + di = dimension_dun->i; + + struct aggregated_weight aw = { + .min = t->value, + .max = t->value, + .sum = t->value, + .count = 1, + .hsp = t->highlighted, + .bsp = t->baseline, + }; + + storage_point_to_json(wb, WPT_DIMENSION, di, ii, ci, ni, &aw, options, baseline); + node_dun->exposed = true; + context_dun->exposed = true; + instance_dun->exposed = true; + dimension_dun->exposed = true; + + merge_into_aw(instance_aw, t); + merge_into_aw(context_aw, t); + merge_into_aw(node_aw, t); + + node_dun->duration_ut += t->duration_ut; + total_dimensions++; + } + dfe_done(t); + + // close instance + if(last_ria) { + storage_point_to_json(wb, WPT_INSTANCE, di, ii, ci, ni, &instance_aw, options, baseline); + instance_dun->exposed = true; + } + + // close context + if(last_rca) { + storage_point_to_json(wb, WPT_CONTEXT, di, ii, ci, ni, &context_aw, options, baseline); + context_dun->exposed = true; + } + + // close node + if(last_host) { + storage_point_to_json(wb, WPT_NODE, di, ii, ci, ni, &node_aw, options, baseline); + node_dun->exposed = true; + } + + buffer_json_array_close(wb); // points + + buffer_json_member_add_object(wb, "dictionaries"); + buffer_json_member_add_array(wb, "nodes"); + { + struct dict_unique_node *dun; + dfe_start_read(dict_nodes, dun) { + if(!dun->exposed) + continue; + + buffer_json_add_array_item_object(wb); + buffer_json_node_add_v2(wb, dun->host, dun->i, dun->duration_ut, true); + buffer_json_object_close(wb); + } + dfe_done(dun); + } + buffer_json_array_close(wb); + + buffer_json_member_add_array(wb, "contexts"); + { + struct dict_unique_name_units *dun; + dfe_start_read(dict_contexts, dun) { + if(!dun->exposed) + continue; + + buffer_json_add_array_item_object(wb); + buffer_json_member_add_string(wb, "id", dun_dfe.name); + buffer_json_member_add_string(wb, "units", dun->units); + buffer_json_member_add_int64(wb, "ci", dun->i); + buffer_json_object_close(wb); + } + dfe_done(dun); + } + buffer_json_array_close(wb); + + buffer_json_member_add_array(wb, "instances"); + { + struct dict_unique_id_name *dun; + dfe_start_read(dict_instances, dun) { + if(!dun->exposed) + continue; + + buffer_json_add_array_item_object(wb); + buffer_json_member_add_string(wb, "id", dun->id); + if(dun->id != dun->name) + buffer_json_member_add_string(wb, "nm", dun->name); + buffer_json_member_add_int64(wb, "ii", dun->i); + buffer_json_object_close(wb); + } + dfe_done(dun); + } + buffer_json_array_close(wb); + + buffer_json_member_add_array(wb, "dimensions"); + { + struct dict_unique_id_name *dun; + dfe_start_read(dict_dimensions, dun) { + if(!dun->exposed) + continue; + + buffer_json_add_array_item_object(wb); + buffer_json_member_add_string(wb, "id", dun->id); + if(dun->id != dun->name) + buffer_json_member_add_string(wb, "nm", dun->name); + buffer_json_member_add_int64(wb, "di", dun->i); + buffer_json_object_close(wb); + } + dfe_done(dun); + } + buffer_json_array_close(wb); + + buffer_json_object_close(wb); //dictionaries + + buffer_json_agents_v2(wb, &qwd->timings, 0, false, true); + buffer_json_member_add_uint64(wb, "correlated_dimensions", total_dimensions); + buffer_json_member_add_uint64(wb, "total_dimensions_count", examined_dimensions); + buffer_json_finalize(wb); + + dictionary_destroy(dict_nodes); + dictionary_destroy(dict_contexts); + dictionary_destroy(dict_instances); + dictionary_destroy(dict_dimensions); + + return total_dimensions; +} + +static size_t registered_results_to_json_multinode_group_by( + DICTIONARY *results, BUFFER *wb, + time_t after, time_t before, + time_t baseline_after, time_t baseline_before, + size_t points, WEIGHTS_METHOD method, + RRDR_TIME_GROUPING group, RRDR_OPTIONS options, uint32_t shifts, + size_t examined_dimensions, struct query_weights_data *qwd, + WEIGHTS_STATS *stats, + struct query_versions *versions) { + buffer_json_initialize(wb, "\"", "\"", 0, true, (options & RRDR_OPTION_MINIFY) ? BUFFER_JSON_OPTIONS_MINIFY : BUFFER_JSON_OPTIONS_DEFAULT); + buffer_json_member_add_uint64(wb, "api", 2); + + results_header_to_json_v2(results, wb, qwd, after, before, baseline_after, baseline_before, + points, method, group, options, shifts, examined_dimensions, + qwd->timings.executed_ut - qwd->timings.received_ut, stats, true); + + version_hashes_api_v2(wb, versions); + + bool baseline = method == WEIGHTS_METHOD_MC_KS2 || method == WEIGHTS_METHOD_MC_VOLUME; + multinode_data_schema(wb, options, "v_schema", baseline, true); + + DICTIONARY *group_by = dictionary_create_advanced(DICT_OPTION_SINGLE_THREADED | DICT_OPTION_DONT_OVERWRITE_VALUE | DICT_OPTION_FIXED_SIZE, + NULL, sizeof(struct aggregated_weight)); + + struct register_result *t; + size_t total_dimensions = 0; + BUFFER *key = buffer_create(0, NULL); + BUFFER *name = buffer_create(0, NULL); + dfe_start_read(results, t) { + + buffer_flush(key); + buffer_flush(name); + + if(qwd->qwr->group_by.group_by & RRDR_GROUP_BY_DIMENSION) { + buffer_strcat(key, rrdmetric_acquired_name(t->rma)); + buffer_strcat(name, rrdmetric_acquired_name(t->rma)); + } + if(qwd->qwr->group_by.group_by & RRDR_GROUP_BY_INSTANCE) { + if(buffer_strlen(key)) { + buffer_fast_strcat(key, ",", 1); + buffer_fast_strcat(name, ",", 1); + } + + buffer_strcat(key, rrdinstance_acquired_id(t->ria)); + buffer_strcat(name, rrdinstance_acquired_name(t->ria)); + + if(!(qwd->qwr->group_by.group_by & RRDR_GROUP_BY_NODE)) { + buffer_fast_strcat(key, "@", 1); + buffer_fast_strcat(name, "@", 1); + buffer_strcat(key, t->host->machine_guid); + buffer_strcat(name, rrdhost_hostname(t->host)); + } + } + if(qwd->qwr->group_by.group_by & RRDR_GROUP_BY_NODE) { + if(buffer_strlen(key)) { + buffer_fast_strcat(key, ",", 1); + buffer_fast_strcat(name, ",", 1); + } + + buffer_strcat(key, t->host->machine_guid); + buffer_strcat(name, rrdhost_hostname(t->host)); + } + if(qwd->qwr->group_by.group_by & RRDR_GROUP_BY_CONTEXT) { + if(buffer_strlen(key)) { + buffer_fast_strcat(key, ",", 1); + buffer_fast_strcat(name, ",", 1); + } + + buffer_strcat(key, rrdcontext_acquired_id(t->rca)); + buffer_strcat(name, rrdcontext_acquired_id(t->rca)); + } + if(qwd->qwr->group_by.group_by & RRDR_GROUP_BY_UNITS) { + if(buffer_strlen(key)) { + buffer_fast_strcat(key, ",", 1); + buffer_fast_strcat(name, ",", 1); + } + + buffer_strcat(key, rrdcontext_acquired_units(t->rca)); + buffer_strcat(name, rrdcontext_acquired_units(t->rca)); + } + + struct aggregated_weight *aw = dictionary_set(group_by, buffer_tostring(key), NULL, sizeof(struct aggregated_weight)); + if(!aw->name) { + aw->name = strdupz(buffer_tostring(name)); + aw->min = aw->max = aw->sum = t->value; + aw->count = 1; + aw->hsp = t->highlighted; + aw->bsp = t->baseline; + } + else + merge_into_aw(*aw, t); + + total_dimensions++; + } + dfe_done(t); + buffer_free(key); key = NULL; + buffer_free(name); name = NULL; + + struct aggregated_weight *aw; + buffer_json_member_add_array(wb, "result"); + dfe_start_read(group_by, aw) { + const char *k = aw_dfe.name; + const char *n = aw->name; + + buffer_json_add_array_item_object(wb); + buffer_json_member_add_string(wb, "id", k); + + if(strcmp(k, n) != 0) + buffer_json_member_add_string(wb, "nm", n); + + storage_point_to_json(wb, WPT_GROUP, 0, 0, 0, 0, aw, options, baseline); + buffer_json_object_close(wb); + + freez((void *)aw->name); + } + dfe_done(aw); + buffer_json_array_close(wb); // result + + buffer_json_agents_v2(wb, &qwd->timings, 0, false, true); + buffer_json_member_add_uint64(wb, "correlated_dimensions", total_dimensions); + buffer_json_member_add_uint64(wb, "total_dimensions_count", examined_dimensions); + buffer_json_finalize(wb); + + dictionary_destroy(group_by); + + return total_dimensions; +} + +// ---------------------------------------------------------------------------- +// KS2 algorithm functions + +typedef long int DIFFS_NUMBERS; +#define DOUBLE_TO_INT_MULTIPLIER 100000 + +static inline int binary_search_bigger_than(const DIFFS_NUMBERS arr[], int left, int size, DIFFS_NUMBERS K) { + // binary search to find the index the smallest index + // of the first value in the array that is greater than K + + int right = size; + while(left < right) { + int middle = (int)(((unsigned int)(left + right)) >> 1); + + if(arr[middle] > K) + right = middle; + + else + left = middle + 1; + } + + return left; +} + +int compare_diffs(const void *left, const void *right) { + DIFFS_NUMBERS lt = *(DIFFS_NUMBERS *)left; + DIFFS_NUMBERS rt = *(DIFFS_NUMBERS *)right; + + // https://stackoverflow.com/a/3886497/1114110 + return (lt > rt) - (lt < rt); +} + +static size_t calculate_pairs_diff(DIFFS_NUMBERS *diffs, NETDATA_DOUBLE *arr, size_t size) { + NETDATA_DOUBLE *last = &arr[size - 1]; + size_t added = 0; + + while(last > arr) { + NETDATA_DOUBLE second = *last--; + NETDATA_DOUBLE first = *last; + *diffs++ = (DIFFS_NUMBERS)((first - second) * (NETDATA_DOUBLE)DOUBLE_TO_INT_MULTIPLIER); + added++; + } + + return added; +} + +static double ks_2samp( + DIFFS_NUMBERS baseline_diffs[], int base_size, + DIFFS_NUMBERS highlight_diffs[], int high_size, + uint32_t base_shifts) { + + qsort(baseline_diffs, base_size, sizeof(DIFFS_NUMBERS), compare_diffs); + qsort(highlight_diffs, high_size, sizeof(DIFFS_NUMBERS), compare_diffs); + + // Now we should be calculating this: + // + // For each number in the diffs arrays, we should find the index of the + // number bigger than them in both arrays and calculate the % of this index + // vs the total array size. Once we have the 2 percentages, we should find + // the min and max across the delta of all of them. + // + // It should look like this: + // + // base_pcent = binary_search_bigger_than(...) / base_size; + // high_pcent = binary_search_bigger_than(...) / high_size; + // delta = base_pcent - high_pcent; + // if(delta < min) min = delta; + // if(delta > max) max = delta; + // + // This would require a lot of multiplications and divisions. + // + // To speed it up, we do the binary search to find the index of each number + // but, then we divide the base index by the power of two number (shifts) it + // is bigger than high index. So the 2 indexes are now comparable. + // We also keep track of the original indexes with min and max, to properly + // calculate their percentages once the loops finish. + + + // initialize min and max using the first number of baseline_diffs + DIFFS_NUMBERS K = baseline_diffs[0]; + int base_idx = binary_search_bigger_than(baseline_diffs, 1, base_size, K); + int high_idx = binary_search_bigger_than(highlight_diffs, 0, high_size, K); + int delta = base_idx - (high_idx << base_shifts); + int min = delta, max = delta; + int base_min_idx = base_idx; + int base_max_idx = base_idx; + int high_min_idx = high_idx; + int high_max_idx = high_idx; + + // do the baseline_diffs starting from 1 (we did position 0 above) + for(int i = 1; i < base_size; i++) { + K = baseline_diffs[i]; + base_idx = binary_search_bigger_than(baseline_diffs, i + 1, base_size, K); // starting from i, since data1 is sorted + high_idx = binary_search_bigger_than(highlight_diffs, 0, high_size, K); + + delta = base_idx - (high_idx << base_shifts); + if(delta < min) { + min = delta; + base_min_idx = base_idx; + high_min_idx = high_idx; + } + else if(delta > max) { + max = delta; + base_max_idx = base_idx; + high_max_idx = high_idx; + } + } + + // do the highlight_diffs starting from 0 + for(int i = 0; i < high_size; i++) { + K = highlight_diffs[i]; + base_idx = binary_search_bigger_than(baseline_diffs, 0, base_size, K); + high_idx = binary_search_bigger_than(highlight_diffs, i + 1, high_size, K); // starting from i, since data2 is sorted + + delta = base_idx - (high_idx << base_shifts); + if(delta < min) { + min = delta; + base_min_idx = base_idx; + high_min_idx = high_idx; + } + else if(delta > max) { + max = delta; + base_max_idx = base_idx; + high_max_idx = high_idx; + } + } + + // now we have the min, max and their indexes + // properly calculate min and max as dmin and dmax + double dbase_size = (double)base_size; + double dhigh_size = (double)high_size; + double dmin = ((double)base_min_idx / dbase_size) - ((double)high_min_idx / dhigh_size); + double dmax = ((double)base_max_idx / dbase_size) - ((double)high_max_idx / dhigh_size); + + dmin = -dmin; + if(islessequal(dmin, 0.0)) dmin = 0.0; + else if(isgreaterequal(dmin, 1.0)) dmin = 1.0; + + double d; + if(isgreaterequal(dmin, dmax)) d = dmin; + else d = dmax; + + double en = round(dbase_size * dhigh_size / (dbase_size + dhigh_size)); + + // under these conditions, KSfbar() crashes + if(unlikely(isnan(en) || isinf(en) || en == 0.0 || isnan(d) || isinf(d))) + return NAN; + + return KSfbar((int)en, d); +} + +static double kstwo( + NETDATA_DOUBLE baseline[], int baseline_points, + NETDATA_DOUBLE highlight[], int highlight_points, + uint32_t base_shifts) { + + // -1 in size, since the calculate_pairs_diffs() returns one less point + DIFFS_NUMBERS baseline_diffs[baseline_points - 1]; + DIFFS_NUMBERS highlight_diffs[highlight_points - 1]; + + int base_size = (int)calculate_pairs_diff(baseline_diffs, baseline, baseline_points); + int high_size = (int)calculate_pairs_diff(highlight_diffs, highlight, highlight_points); + + if(unlikely(!base_size || !high_size)) + return NAN; + + if(unlikely(base_size != baseline_points - 1 || high_size != highlight_points - 1)) { + netdata_log_error("Metric correlations: internal error - calculate_pairs_diff() returns the wrong number of entries"); + return NAN; + } + + return ks_2samp(baseline_diffs, base_size, highlight_diffs, high_size, base_shifts); +} + +NETDATA_DOUBLE *rrd2rrdr_ks2( + ONEWAYALLOC *owa, RRDHOST *host, + RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma, + time_t after, time_t before, size_t points, RRDR_OPTIONS options, + RRDR_TIME_GROUPING time_group_method, const char *time_group_options, size_t tier, + WEIGHTS_STATS *stats, + size_t *entries, + STORAGE_POINT *sp + ) { + + NETDATA_DOUBLE *ret = NULL; + + QUERY_TARGET_REQUEST qtr = { + .version = 1, + .host = host, + .rca = rca, + .ria = ria, + .rma = rma, + .after = after, + .before = before, + .points = points, + .options = options, + .time_group_method = time_group_method, + .time_group_options = time_group_options, + .tier = tier, + .query_source = QUERY_SOURCE_API_WEIGHTS, + .priority = STORAGE_PRIORITY_SYNCHRONOUS, + }; + + QUERY_TARGET *qt = query_target_create(&qtr); + RRDR *r = rrd2rrdr(owa, qt); + if(!r) + goto cleanup; + + stats->db_queries++; + stats->result_points += r->stats.result_points_generated; + stats->db_points += r->stats.db_points_read; + for(size_t tr = 0; tr < storage_tiers ; tr++) + stats->db_points_per_tier[tr] += r->internal.qt->db.tiers[tr].points; + + if(r->d != 1 || r->internal.qt->query.used != 1) { + netdata_log_error("WEIGHTS: on query '%s' expected 1 dimension in RRDR but got %zu r->d and %zu qt->query.used", + r->internal.qt->id, r->d, (size_t)r->internal.qt->query.used); + goto cleanup; + } + + if(unlikely(r->od[0] & RRDR_DIMENSION_HIDDEN)) + goto cleanup; + + if(unlikely(!(r->od[0] & RRDR_DIMENSION_QUERIED))) + goto cleanup; + + if(unlikely(!(r->od[0] & RRDR_DIMENSION_NONZERO))) + goto cleanup; + + if(rrdr_rows(r) < 2) + goto cleanup; + + *entries = rrdr_rows(r); + ret = onewayalloc_mallocz(owa, sizeof(NETDATA_DOUBLE) * rrdr_rows(r)); + + if(sp) + *sp = r->internal.qt->query.array[0].query_points; + + // copy the points of the dimension to a contiguous array + // there is no need to check for empty values, since empty values are already zero + // https://github.com/netdata/netdata/blob/6e3144683a73a2024d51425b20ecfd569034c858/web/api/queries/average/average.c#L41-L43 + memcpy(ret, r->v, rrdr_rows(r) * sizeof(NETDATA_DOUBLE)); + +cleanup: + rrdr_free(owa, r); + query_target_release(qt); + return ret; +} + +static void rrdset_metric_correlations_ks2( + RRDHOST *host, + RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma, + DICTIONARY *results, + time_t baseline_after, time_t baseline_before, + time_t after, time_t before, + size_t points, RRDR_OPTIONS options, + RRDR_TIME_GROUPING time_group_method, const char *time_group_options, size_t tier, + uint32_t shifts, + WEIGHTS_STATS *stats, bool register_zero + ) { + + options |= RRDR_OPTION_NATURAL_POINTS; + + usec_t started_ut = now_monotonic_usec(); + ONEWAYALLOC *owa = onewayalloc_create(16 * 1024); + + size_t high_points = 0; + STORAGE_POINT highlighted_sp; + NETDATA_DOUBLE *highlight = rrd2rrdr_ks2( + owa, host, rca, ria, rma, after, before, points, + options, time_group_method, time_group_options, tier, stats, &high_points, &highlighted_sp); + + if(!highlight) + goto cleanup; + + size_t base_points = 0; + STORAGE_POINT baseline_sp; + NETDATA_DOUBLE *baseline = rrd2rrdr_ks2( + owa, host, rca, ria, rma, baseline_after, baseline_before, high_points << shifts, + options, time_group_method, time_group_options, tier, stats, &base_points, &baseline_sp); + + if(!baseline) + goto cleanup; + + stats->binary_searches += 2 * (base_points - 1) + 2 * (high_points - 1); + + double prob = kstwo(baseline, (int)base_points, highlight, (int)high_points, shifts); + if(!isnan(prob) && !isinf(prob)) { + + // these conditions should never happen, but still let's check + if(unlikely(prob < 0.0)) { + netdata_log_error("Metric correlations: kstwo() returned a negative number: %f", prob); + prob = -prob; + } + if(unlikely(prob > 1.0)) { + netdata_log_error("Metric correlations: kstwo() returned a number above 1.0: %f", prob); + prob = 1.0; + } + + usec_t ended_ut = now_monotonic_usec(); + + // to spread the results evenly, 0.0 needs to be the less correlated and 1.0 the most correlated + // so, we flip the result of kstwo() + register_result(results, host, rca, ria, rma, 1.0 - prob, RESULT_IS_BASE_HIGH_RATIO, &highlighted_sp, + &baseline_sp, stats, register_zero, ended_ut - started_ut); + } + +cleanup: + onewayalloc_destroy(owa); +} + +// ---------------------------------------------------------------------------- +// VOLUME algorithm functions + +static void merge_query_value_to_stats(QUERY_VALUE *qv, WEIGHTS_STATS *stats, size_t queries) { + stats->db_queries += queries; + stats->result_points += qv->result_points; + stats->db_points += qv->points_read; + for(size_t tier = 0; tier < storage_tiers ; tier++) + stats->db_points_per_tier[tier] += qv->storage_points_per_tier[tier]; +} + +static void rrdset_metric_correlations_volume( + RRDHOST *host, + RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma, + DICTIONARY *results, + time_t baseline_after, time_t baseline_before, + time_t after, time_t before, + RRDR_OPTIONS options, RRDR_TIME_GROUPING time_group_method, const char *time_group_options, + size_t tier, + WEIGHTS_STATS *stats, bool register_zero) { + + options |= RRDR_OPTION_MATCH_IDS | RRDR_OPTION_ABSOLUTE | RRDR_OPTION_NATURAL_POINTS; + + QUERY_VALUE baseline_average = rrdmetric2value(host, rca, ria, rma, baseline_after, baseline_before, + options, time_group_method, time_group_options, tier, 0, + QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_SYNCHRONOUS); + merge_query_value_to_stats(&baseline_average, stats, 1); + + if(!netdata_double_isnumber(baseline_average.value)) { + // this means no data for the baseline window, but we may have data for the highlighted one - assume zero + baseline_average.value = 0.0; + } + + QUERY_VALUE highlight_average = rrdmetric2value(host, rca, ria, rma, after, before, + options, time_group_method, time_group_options, tier, 0, + QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_SYNCHRONOUS); + merge_query_value_to_stats(&highlight_average, stats, 1); + + if(!netdata_double_isnumber(highlight_average.value)) + return; + + if(baseline_average.value == highlight_average.value) { + // they are the same - let's move on + return; + } + + if((options & RRDR_OPTION_ANOMALY_BIT) && highlight_average.value < baseline_average.value) { + // when working on anomaly bits, we are looking for an increase in the anomaly rate + return; + } + + char highlight_countif_options[50 + 1]; + snprintfz(highlight_countif_options, 50, "%s" NETDATA_DOUBLE_FORMAT, highlight_average.value < baseline_average.value ? "<" : ">", baseline_average.value); + QUERY_VALUE highlight_countif = rrdmetric2value(host, rca, ria, rma, after, before, + options, RRDR_GROUPING_COUNTIF, highlight_countif_options, tier, 0, + QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_SYNCHRONOUS); + merge_query_value_to_stats(&highlight_countif, stats, 1); + + if(!netdata_double_isnumber(highlight_countif.value)) { + netdata_log_info("WEIGHTS: highlighted countif query failed, but highlighted average worked - strange..."); + return; + } + + // this represents the percentage of time + // the highlighted window was above/below the baseline window + // (above or below depending on their averages) + highlight_countif.value = highlight_countif.value / 100.0; // countif returns 0 - 100.0 + + RESULT_FLAGS flags; + NETDATA_DOUBLE pcent = NAN; + if(isgreater(baseline_average.value, 0.0) || isless(baseline_average.value, 0.0)) { + flags = RESULT_IS_BASE_HIGH_RATIO; + pcent = (highlight_average.value - baseline_average.value) / baseline_average.value * highlight_countif.value; + } + else { + flags = RESULT_IS_PERCENTAGE_OF_TIME; + pcent = highlight_countif.value; + } + + register_result(results, host, rca, ria, rma, pcent, flags, &highlight_average.sp, &baseline_average.sp, stats, + register_zero, baseline_average.duration_ut + highlight_average.duration_ut + highlight_countif.duration_ut); +} + +// ---------------------------------------------------------------------------- +// VALUE / ANOMALY RATE algorithm functions + +static void rrdset_weights_value( + RRDHOST *host, + RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma, + DICTIONARY *results, + time_t after, time_t before, + RRDR_OPTIONS options, RRDR_TIME_GROUPING time_group_method, const char *time_group_options, + size_t tier, + WEIGHTS_STATS *stats, bool register_zero) { + + options |= RRDR_OPTION_MATCH_IDS | RRDR_OPTION_NATURAL_POINTS; + + QUERY_VALUE qv = rrdmetric2value(host, rca, ria, rma, after, before, + options, time_group_method, time_group_options, tier, 0, + QUERY_SOURCE_API_WEIGHTS, STORAGE_PRIORITY_SYNCHRONOUS); + + merge_query_value_to_stats(&qv, stats, 1); + + if(netdata_double_isnumber(qv.value)) + register_result(results, host, rca, ria, rma, qv.value, 0, &qv.sp, NULL, stats, register_zero, qv.duration_ut); +} + +static void rrdset_weights_multi_dimensional_value(struct query_weights_data *qwd) { + QUERY_TARGET_REQUEST qtr = { + .version = 1, + .scope_nodes = qwd->qwr->scope_nodes, + .scope_contexts = qwd->qwr->scope_contexts, + .nodes = qwd->qwr->nodes, + .contexts = qwd->qwr->contexts, + .instances = qwd->qwr->instances, + .dimensions = qwd->qwr->dimensions, + .labels = qwd->qwr->labels, + .alerts = qwd->qwr->alerts, + .after = qwd->qwr->after, + .before = qwd->qwr->before, + .points = 1, + .options = qwd->qwr->options | RRDR_OPTION_NATURAL_POINTS, + .time_group_method = qwd->qwr->time_group_method, + .time_group_options = qwd->qwr->time_group_options, + .tier = qwd->qwr->tier, + .timeout_ms = qwd->qwr->timeout_ms, + .query_source = QUERY_SOURCE_API_WEIGHTS, + .priority = STORAGE_PRIORITY_NORMAL, + }; + + ONEWAYALLOC *owa = onewayalloc_create(16 * 1024); + QUERY_TARGET *qt = query_target_create(&qtr); + RRDR *r = rrd2rrdr(owa, qt); + + if(!r || rrdr_rows(r) != 1 || !r->d || r->d != r->internal.qt->query.used) + goto cleanup; + + QUERY_VALUE qv = { + .after = r->view.after, + .before = r->view.before, + .points_read = r->stats.db_points_read, + .result_points = r->stats.result_points_generated, + }; + + size_t queries = 0; + for(size_t d = 0; d < r->d ;d++) { + if(!rrdr_dimension_should_be_exposed(r->od[d], qwd->qwr->options)) + continue; + + long i = 0; // only one row + NETDATA_DOUBLE *cn = &r->v[ i * r->d ]; + NETDATA_DOUBLE *ar = &r->ar[ i * r->d ]; + + qv.value = cn[d]; + qv.anomaly_rate = ar[d]; + storage_point_merge_to(qv.sp, r->internal.qt->query.array[d].query_points); + + if(netdata_double_isnumber(qv.value)) { + QUERY_METRIC *qm = query_metric(r->internal.qt, d); + QUERY_DIMENSION *qd = query_dimension(r->internal.qt, qm->link.query_dimension_id); + QUERY_INSTANCE *qi = query_instance(r->internal.qt, qm->link.query_instance_id); + QUERY_CONTEXT *qc = query_context(r->internal.qt, qm->link.query_context_id); + QUERY_NODE *qn = query_node(r->internal.qt, qm->link.query_node_id); + + register_result(qwd->results, qn->rrdhost, qc->rca, qi->ria, qd->rma, qv.value, 0, &qv.sp, + NULL, &qwd->stats, qwd->register_zero, qm->duration_ut); + } + + queries++; + } + + merge_query_value_to_stats(&qv, &qwd->stats, queries); + +cleanup: + rrdr_free(owa, r); + query_target_release(qt); + onewayalloc_destroy(owa); +} + +// ---------------------------------------------------------------------------- + +int compare_netdata_doubles(const void *left, const void *right) { + NETDATA_DOUBLE lt = *(NETDATA_DOUBLE *)left; + NETDATA_DOUBLE rt = *(NETDATA_DOUBLE *)right; + + // https://stackoverflow.com/a/3886497/1114110 + return (lt > rt) - (lt < rt); +} + +static inline int binary_search_bigger_than_netdata_double(const NETDATA_DOUBLE arr[], int left, int size, NETDATA_DOUBLE K) { + // binary search to find the index the smallest index + // of the first value in the array that is greater than K + + int right = size; + while(left < right) { + int middle = (int)(((unsigned int)(left + right)) >> 1); + + if(arr[middle] > K) + right = middle; + + else + left = middle + 1; + } + + return left; +} + +// ---------------------------------------------------------------------------- +// spread the results evenly according to their value + +static size_t spread_results_evenly(DICTIONARY *results, WEIGHTS_STATS *stats) { + struct register_result *t; + + // count the dimensions + size_t dimensions = dictionary_entries(results); + if(!dimensions) return 0; + + if(stats->max_base_high_ratio == 0.0) + stats->max_base_high_ratio = 1.0; + + // create an array of the right size and copy all the values in it + NETDATA_DOUBLE slots[dimensions]; + dimensions = 0; + dfe_start_read(results, t) { + if(t->flags & RESULT_IS_PERCENTAGE_OF_TIME) + t->value = t->value * stats->max_base_high_ratio; + + slots[dimensions++] = t->value; + } + dfe_done(t); + + if(!dimensions) return 0; // Coverity fix + + // sort the array with the values of all dimensions + qsort(slots, dimensions, sizeof(NETDATA_DOUBLE), compare_netdata_doubles); + + // skip the duplicates in the sorted array + NETDATA_DOUBLE last_value = NAN; + size_t unique_values = 0; + for(size_t i = 0; i < dimensions ;i++) { + if(likely(slots[i] != last_value)) + slots[unique_values++] = last_value = slots[i]; + } + + // this cannot happen, but coverity thinks otherwise... + if(!unique_values) + unique_values = dimensions; + + // calculate the weight of each slot, using the number of unique values + NETDATA_DOUBLE slot_weight = 1.0 / (NETDATA_DOUBLE)unique_values; + + dfe_start_read(results, t) { + int slot = binary_search_bigger_than_netdata_double(slots, 0, (int)unique_values, t->value); + NETDATA_DOUBLE v = slot * slot_weight; + if(unlikely(v > 1.0)) v = 1.0; + v = 1.0 - v; + t->value = v; + } + dfe_done(t); + + return dimensions; +} + +// ---------------------------------------------------------------------------- +// The main function + +static ssize_t weights_for_rrdmetric(void *data, RRDHOST *host, RRDCONTEXT_ACQUIRED *rca, RRDINSTANCE_ACQUIRED *ria, RRDMETRIC_ACQUIRED *rma) { + struct query_weights_data *qwd = data; + QUERY_WEIGHTS_REQUEST *qwr = qwd->qwr; + + if(qwd->qwr->interrupt_callback && qwd->qwr->interrupt_callback(qwd->qwr->interrupt_callback_data)) { + qwd->interrupted = true; + return -1; + } + + qwd->examined_dimensions++; + + switch(qwr->method) { + case WEIGHTS_METHOD_VALUE: + rrdset_weights_value( + host, rca, ria, rma, + qwd->results, + qwr->after, qwr->before, + qwr->options, qwr->time_group_method, qwr->time_group_options, qwr->tier, + &qwd->stats, qwd->register_zero + ); + break; + + case WEIGHTS_METHOD_ANOMALY_RATE: + qwr->options |= RRDR_OPTION_ANOMALY_BIT; + rrdset_weights_value( + host, rca, ria, rma, + qwd->results, + qwr->after, qwr->before, + qwr->options, qwr->time_group_method, qwr->time_group_options, qwr->tier, + &qwd->stats, qwd->register_zero + ); + break; + + case WEIGHTS_METHOD_MC_VOLUME: + rrdset_metric_correlations_volume( + host, rca, ria, rma, + qwd->results, + qwr->baseline_after, qwr->baseline_before, + qwr->after, qwr->before, + qwr->options, qwr->time_group_method, qwr->time_group_options, qwr->tier, + &qwd->stats, qwd->register_zero + ); + break; + + default: + case WEIGHTS_METHOD_MC_KS2: + rrdset_metric_correlations_ks2( + host, rca, ria, rma, + qwd->results, + qwr->baseline_after, qwr->baseline_before, + qwr->after, qwr->before, qwr->points, + qwr->options, qwr->time_group_method, qwr->time_group_options, qwr->tier, qwd->shifts, + &qwd->stats, qwd->register_zero + ); + break; + } + + qwd->timings.executed_ut = now_monotonic_usec(); + if(qwd->timings.executed_ut - qwd->timings.received_ut > qwd->timeout_us) { + qwd->timed_out = true; + return -1; + } + + query_progress_done_step(qwr->transaction, 1); + + return 1; +} + +static ssize_t weights_do_context_callback(void *data, RRDCONTEXT_ACQUIRED *rca, bool queryable_context) { + if(!queryable_context) + return false; + + struct query_weights_data *qwd = data; + + bool has_retention = false; + switch(qwd->qwr->method) { + case WEIGHTS_METHOD_VALUE: + case WEIGHTS_METHOD_ANOMALY_RATE: + has_retention = rrdcontext_retention_match(rca, qwd->qwr->after, qwd->qwr->before); + break; + + case WEIGHTS_METHOD_MC_KS2: + case WEIGHTS_METHOD_MC_VOLUME: + has_retention = rrdcontext_retention_match(rca, qwd->qwr->after, qwd->qwr->before); + if(has_retention) + has_retention = rrdcontext_retention_match(rca, qwd->qwr->baseline_after, qwd->qwr->baseline_before); + break; + } + + if(!has_retention) + return 0; + + ssize_t ret = weights_foreach_rrdmetric_in_context(rca, + qwd->instances_sp, + NULL, + qwd->labels_sp, + qwd->alerts_sp, + qwd->dimensions_sp, + true, true, qwd->qwr->version, + weights_for_rrdmetric, qwd); + return ret; +} + +ssize_t weights_do_node_callback(void *data, RRDHOST *host, bool queryable) { + if(!queryable) + return 0; + + struct query_weights_data *qwd = data; + + ssize_t ret = query_scope_foreach_context(host, qwd->qwr->scope_contexts, + qwd->scope_contexts_sp, qwd->contexts_sp, + weights_do_context_callback, queryable, qwd); + + return ret; +} + +int web_api_v12_weights(BUFFER *wb, QUERY_WEIGHTS_REQUEST *qwr) { + + char *error = NULL; + int resp = HTTP_RESP_OK; + + // if the user didn't give a timeout + // assume 60 seconds + if(!qwr->timeout_ms) + qwr->timeout_ms = 5 * 60 * MSEC_PER_SEC; + + // if the timeout is less than 1 second + // make it at least 1 second + if(qwr->timeout_ms < (long)(1 * MSEC_PER_SEC)) + qwr->timeout_ms = 1 * MSEC_PER_SEC; + + struct query_weights_data qwd = { + .qwr = qwr, + + .scope_nodes_sp = string_to_simple_pattern(qwr->scope_nodes), + .scope_contexts_sp = string_to_simple_pattern(qwr->scope_contexts), + .nodes_sp = string_to_simple_pattern(qwr->nodes), + .contexts_sp = string_to_simple_pattern(qwr->contexts), + .instances_sp = string_to_simple_pattern(qwr->instances), + .dimensions_sp = string_to_simple_pattern(qwr->dimensions), + .labels_sp = string_to_simple_pattern(qwr->labels), + .alerts_sp = string_to_simple_pattern(qwr->alerts), + .timeout_us = qwr->timeout_ms * USEC_PER_MS, + .timed_out = false, + .examined_dimensions = 0, + .register_zero = true, + .results = register_result_init(), + .stats = {}, + .shifts = 0, + .timings = { + .received_ut = now_monotonic_usec(), + } + }; + + if(!rrdr_relative_window_to_absolute_query(&qwr->after, &qwr->before, NULL, false)) + buffer_no_cacheable(wb); + else + buffer_cacheable(wb); + + if (qwr->before <= qwr->after) { + resp = HTTP_RESP_BAD_REQUEST; + error = "Invalid selected time-range."; + goto cleanup; + } + + if(qwr->method == WEIGHTS_METHOD_MC_KS2 || qwr->method == WEIGHTS_METHOD_MC_VOLUME) { + if(!qwr->points) qwr->points = 500; + + if(qwr->baseline_before <= API_RELATIVE_TIME_MAX) + qwr->baseline_before += qwr->after; + + rrdr_relative_window_to_absolute_query(&qwr->baseline_after, &qwr->baseline_before, NULL, false); + + if (qwr->baseline_before <= qwr->baseline_after) { + resp = HTTP_RESP_BAD_REQUEST; + error = "Invalid baseline time-range."; + goto cleanup; + } + + // baseline should be a power of two multiple of highlight + long long base_delta = qwr->baseline_before - qwr->baseline_after; + long long high_delta = qwr->before - qwr->after; + uint32_t multiplier = (uint32_t)round((double)base_delta / (double)high_delta); + + // check if the multiplier is a power of two + // https://stackoverflow.com/a/600306/1114110 + if((multiplier & (multiplier - 1)) != 0) { + // it is not power of two + // let's find the closest power of two + // https://stackoverflow.com/a/466242/1114110 + multiplier--; + multiplier |= multiplier >> 1; + multiplier |= multiplier >> 2; + multiplier |= multiplier >> 4; + multiplier |= multiplier >> 8; + multiplier |= multiplier >> 16; + multiplier++; + } + + // convert the multiplier to the number of shifts + // we need to do, to divide baseline numbers to match + // the highlight ones + while(multiplier > 1) { + qwd.shifts++; + multiplier = multiplier >> 1; + } + + // if the baseline size will not comply to MAX_POINTS + // lower the window of the baseline + while(qwd.shifts && (qwr->points << qwd.shifts) > MAX_POINTS) + qwd.shifts--; + + // if the baseline size still does not comply to MAX_POINTS + // lower the resolution of the highlight and the baseline + while((qwr->points << qwd.shifts) > MAX_POINTS) + qwr->points = qwr->points >> 1; + + if(qwr->points < 15) { + resp = HTTP_RESP_BAD_REQUEST; + error = "Too few points available, at least 15 are needed."; + goto cleanup; + } + + // adjust the baseline to be multiplier times bigger than the highlight + qwr->baseline_after = qwr->baseline_before - (high_delta << qwd.shifts); + } + + if(qwr->options & RRDR_OPTION_NONZERO) { + qwd.register_zero = false; + + // remove it to run the queries without it + qwr->options &= ~RRDR_OPTION_NONZERO; + } + + if(qwr->host && qwr->version == 1) + weights_do_node_callback(&qwd, qwr->host, true); + else { + if((qwd.qwr->method == WEIGHTS_METHOD_VALUE || qwd.qwr->method == WEIGHTS_METHOD_ANOMALY_RATE) && (qwd.contexts_sp || qwd.scope_contexts_sp)) { + rrdset_weights_multi_dimensional_value(&qwd); + } + else { + query_scope_foreach_host(qwd.scope_nodes_sp, qwd.nodes_sp, + weights_do_node_callback, &qwd, + &qwd.versions, + NULL); + } + } + + if(!qwd.register_zero) { + // put it back, to show it in the response + qwr->options |= RRDR_OPTION_NONZERO; + } + + if(qwd.timed_out) { + error = "timed out"; + resp = HTTP_RESP_GATEWAY_TIMEOUT; + goto cleanup; + } + + if(qwd.interrupted) { + error = "interrupted"; + resp = HTTP_RESP_CLIENT_CLOSED_REQUEST; + goto cleanup; + } + + if(!qwd.register_zero) + qwr->options |= RRDR_OPTION_NONZERO; + + if(!(qwr->options & RRDR_OPTION_RETURN_RAW) && qwr->method != WEIGHTS_METHOD_VALUE) + spread_results_evenly(qwd.results, &qwd.stats); + + usec_t ended_usec = qwd.timings.executed_ut = now_monotonic_usec(); + + // generate the json output we need + buffer_flush(wb); + + size_t added_dimensions = 0; + switch(qwr->format) { + case WEIGHTS_FORMAT_CHARTS: + added_dimensions = + registered_results_to_json_charts( + qwd.results, wb, + qwr->after, qwr->before, + qwr->baseline_after, qwr->baseline_before, + qwr->points, qwr->method, qwr->time_group_method, qwr->options, qwd.shifts, + qwd.examined_dimensions, + ended_usec - qwd.timings.received_ut, &qwd.stats); + break; + + case WEIGHTS_FORMAT_CONTEXTS: + added_dimensions = + registered_results_to_json_contexts( + qwd.results, wb, + qwr->after, qwr->before, + qwr->baseline_after, qwr->baseline_before, + qwr->points, qwr->method, qwr->time_group_method, qwr->options, qwd.shifts, + qwd.examined_dimensions, + ended_usec - qwd.timings.received_ut, &qwd.stats); + break; + + default: + case WEIGHTS_FORMAT_MULTINODE: + // we don't support these groupings in weights + qwr->group_by.group_by &= ~(RRDR_GROUP_BY_LABEL|RRDR_GROUP_BY_SELECTED|RRDR_GROUP_BY_PERCENTAGE_OF_INSTANCE); + if(qwr->group_by.group_by == RRDR_GROUP_BY_NONE) { + added_dimensions = + registered_results_to_json_multinode_no_group_by( + qwd.results, wb, + qwr->after, qwr->before, + qwr->baseline_after, qwr->baseline_before, + qwr->points, qwr->method, qwr->time_group_method, qwr->options, qwd.shifts, + qwd.examined_dimensions, + &qwd, &qwd.stats, &qwd.versions); + } + else { + added_dimensions = + registered_results_to_json_multinode_group_by( + qwd.results, wb, + qwr->after, qwr->before, + qwr->baseline_after, qwr->baseline_before, + qwr->points, qwr->method, qwr->time_group_method, qwr->options, qwd.shifts, + qwd.examined_dimensions, + &qwd, &qwd.stats, &qwd.versions); + } + break; + } + + if(!added_dimensions && qwr->version < 2) { + error = "no results produced."; + resp = HTTP_RESP_NOT_FOUND; + } + +cleanup: + simple_pattern_free(qwd.scope_nodes_sp); + simple_pattern_free(qwd.scope_contexts_sp); + simple_pattern_free(qwd.nodes_sp); + simple_pattern_free(qwd.contexts_sp); + simple_pattern_free(qwd.instances_sp); + simple_pattern_free(qwd.dimensions_sp); + simple_pattern_free(qwd.labels_sp); + simple_pattern_free(qwd.alerts_sp); + + register_result_destroy(qwd.results); + + if(error) { + buffer_flush(wb); + buffer_sprintf(wb, "{\"error\": \"%s\" }", error); + } + + return resp; +} + +// ---------------------------------------------------------------------------- +// unittest + +/* + +Unit tests against the output of this: + +https://github.com/scipy/scipy/blob/4cf21e753cf937d1c6c2d2a0e372fbc1dbbeea81/scipy/stats/_stats_py.py#L7275-L7449 + +import matplotlib.pyplot as plt +import pandas as pd +import numpy as np +import scipy as sp +from scipy import stats + +data1 = np.array([ 1111, -2222, 33, 100, 100, 15555, -1, 19999, 888, 755, -1, -730 ]) +data2 = np.array([365, -123, 0]) +data1 = np.sort(data1) +data2 = np.sort(data2) +n1 = data1.shape[0] +n2 = data2.shape[0] +data_all = np.concatenate([data1, data2]) +cdf1 = np.searchsorted(data1, data_all, side='right') / n1 +cdf2 = np.searchsorted(data2, data_all, side='right') / n2 +print(data_all) +print("\ndata1", data1, cdf1) +print("\ndata2", data2, cdf2) +cddiffs = cdf1 - cdf2 +print("\ncddiffs", cddiffs) +minS = np.clip(-np.min(cddiffs), 0, 1) +maxS = np.max(cddiffs) +print("\nmin", minS) +print("max", maxS) +m, n = sorted([float(n1), float(n2)], reverse=True) +en = m * n / (m + n) +d = max(minS, maxS) +prob = stats.distributions.kstwo.sf(d, np.round(en)) +print("\nprob", prob) + +*/ + +static int double_expect(double v, const char *str, const char *descr) { + char buf[100 + 1]; + snprintfz(buf, sizeof(buf) - 1, "%0.6f", v); + int ret = strcmp(buf, str) ? 1 : 0; + + fprintf(stderr, "%s %s, expected %s, got %s\n", ret?"FAILED":"OK", descr, str, buf); + return ret; +} + +static int mc_unittest1(void) { + int bs = 3, hs = 3; + DIFFS_NUMBERS base[3] = { 1, 2, 3 }; + DIFFS_NUMBERS high[3] = { 3, 4, 6 }; + + double prob = ks_2samp(base, bs, high, hs, 0); + return double_expect(prob, "0.222222", "3x3"); +} + +static int mc_unittest2(void) { + int bs = 6, hs = 3; + DIFFS_NUMBERS base[6] = { 1, 2, 3, 10, 10, 15 }; + DIFFS_NUMBERS high[3] = { 3, 4, 6 }; + + double prob = ks_2samp(base, bs, high, hs, 1); + return double_expect(prob, "0.500000", "6x3"); +} + +static int mc_unittest3(void) { + int bs = 12, hs = 3; + DIFFS_NUMBERS base[12] = { 1, 2, 3, 10, 10, 15, 111, 19999, 8, 55, -1, -73 }; + DIFFS_NUMBERS high[3] = { 3, 4, 6 }; + + double prob = ks_2samp(base, bs, high, hs, 2); + return double_expect(prob, "0.347222", "12x3"); +} + +static int mc_unittest4(void) { + int bs = 12, hs = 3; + DIFFS_NUMBERS base[12] = { 1111, -2222, 33, 100, 100, 15555, -1, 19999, 888, 755, -1, -730 }; + DIFFS_NUMBERS high[3] = { 365, -123, 0 }; + + double prob = ks_2samp(base, bs, high, hs, 2); + return double_expect(prob, "0.777778", "12x3"); +} + +int mc_unittest(void) { + int errors = 0; + + errors += mc_unittest1(); + errors += mc_unittest2(); + errors += mc_unittest3(); + errors += mc_unittest4(); + + return errors; +} + |